MapReduce query processing systems translate a query statement into a query plan, consisting of a set of MapReduce jobs to be executed in distributed machines. During query translation, these query systems uniformly allocate computing resources to each job by delegating the same tuning to the entire query plan. However, jobs may implement their own collection of operators, which lead to different usage of computing resources. In this paper we propose an adaptive tuning mechanism that enables setting specific resources to each job within a query plan. Our adaptive mechanism relies on a data structure that maps jobs to tuning codes by analyzing source code and log files. This adaptive mechanism allows delegating specific resources to the query plan at runtime as the data structure hosts specific precomputed tuning codes.